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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Structure of a Gene01:30

Structure of a Gene

A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...
Co-activators and Co-repressors02:04

Co-activators and Co-repressors

Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...

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Related Experiment Video

Updated: May 19, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Identifying multi-layer gene regulatory modules from multi-dimensional genomic data.

Wenyuan Li1, Shihua Zhang, Chun-Chi Liu

  • 1Program in Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.

Bioinformatics (Oxford, England)
|August 7, 2012
PubMed
Summary
This summary is machine-generated.

We developed a new method, sparse Multi-Block Partial Least Squares (sMBPLS), to analyze multi-dimensional genomic data. This approach identifies regulatory modules, revealing coupled impacts of copy number variation, DNA methylation, and microRNA on gene expression.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Related Experiment Videos

Last Updated: May 19, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Eukaryotic gene expression is controlled by multi-layer regulations.
  • Emerging multi-dimensional genomic datasets offer insights into cross-layer regulatory interplay.
  • Existing analysis methods for these complex datasets are limited.

Purpose of the Study:

  • To introduce a novel sparse Multi-Block Partial Least Squares (sMBPLS) regression method.
  • To identify multi-dimensional regulatory modules from integrated genomic data.
  • To analyze the interplay of different genomic layers in gene expression regulation.

Main Methods:

  • Developed and applied the sparse Multi-Block Partial Least Squares (sMBPLS) regression method.
  • Utilized multi-dimensional genomic data including copy number variation (CNV), DNA methylation (DM), gene expression (GE), and microRNA expression (ME).
  • Validated the method on simulated data and The Cancer Genome Atlas (TCGA) Ovarian Cancer dataset (230 samples).

Main Results:

  • Identified multi-dimensional regulatory modules comprising factors from different genomic layers.
  • Demonstrated superior functional and transcriptional enrichment of identified modules compared to single-data-type approaches.
  • Revealed coupled impacts of CNV, DM, and microRNA on oncogenes and tumor suppressor genes through network analysis.

Conclusions:

  • The sMBPLS method effectively identifies coordinated regulatory modules from multi-dimensional genomic data.
  • Integrated analysis of genomic layers provides deeper insights into gene expression control.
  • CNV, DM, and microRNA collectively influence key cancer-related genes.